1. Guidelines for the reliable use of high throughput sequencing technologies to detect plant pathogens and pestsSébastien Massart, Ian Adams, Maher Al Rwahnih, Steve Baeyen, Guillaume J. Bilodeau, Arnaud G. Blouin, Neil Boonham, Thierry Candresse, Anne Chandellier, Kris De Jonghe, Denis Kutnjak, Nataša Mehle, 2022, review article Abstract: High-throughput sequencing (HTS) technologies have the potential to become one of the most significant advances in molecular diagnostics. Their use by researchers to detect and characterize plant pathogens and pests has been growing steadily for more than a decade and they are now envisioned as a routine diagnostic test to be deployed by plant pest diagnostics laboratories. Nevertheless, HTS technologies and downstream bioinformatics analysis of the generated datasets represent a complex process including many steps whose reliability must be ensured. The aim of the present guidelines is to provide recommendations for researchers and diagnosticians aiming to reliably use HTS technologies to detect plant pathogens and pests. These guidelines are generic and do not depend on the sequencing technology or platform. They cover all the adoption processes of HTS technologies from test selection to test validation as well as their routine implementation. A special emphasis is given to key elements to be considered: undertaking a risk analysis, designing sample panels for validation, using proper controls, evaluating performance criteria, confirming and interpreting results. These guidelines cover any HTS test used for the detection and identification of any plant pest (viroid, virus, bacteria, phytoplasma, fungi and fungus-like protists, nematodes, arthropods, plants) from any type of matrix. Overall, their adoption by diagnosticians and researchers should greatly improve the reliability of pathogens and pest diagnostics and foster the use of HTS technologies in plant health.
Published in DiRROS: 05.08.2024; Views: 31; Downloads: 74
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2. Looking beyond virus detection in RNA sequencing data : lessons learned from a community-based effort to detect cellular plant pathogens and pestsAnnelies Haegeman, Yoika Foucart, Kris De Jonghe, Thomas Goedefroit, Maher Al Rwahnih, Neil Boonham, Thierry Candresse, Yahya Gaafar, Oscar Hurtado-Gonzales, Zala Kogej Zwitter, Denis Kutnjak, Janja Lamovšek, Irena Mavrič Pleško, 2023, original scientific article Abstract: High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.
Keywords: plant viruses, plant virus detection, plant virology, high-throughput sequencing, RNA sequencing, plant tissues, plant pathogen, diagnostics, high-throughput sequencing, metagenomics, metatranscriptomics
Published in DiRROS: 12.07.2024; Views: 118; Downloads: 97
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